lamhieu/medical_advice_dialogue_en

---
dataset_info:
features:
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 2396505
num_examples: 8676
download_size: 970141
dataset_size: 2396505
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
task_categories:
- text-generation
- text2text-generation
language:
- en
size_categories:
- 1K<n<10K
---

## Description

The dataset is from [medalpaca/medical_meadow_health_advice](https://huggingface.co/datasets/medalpaca/medical_meadow_health_advice), formatted as dialogues for speed and ease of use. Many thanks to author for releasing it.
Importantly, this format is easy to use via the default chat template of `transformers`, meaning you can use [huggingface/alignment-handbook](https://github.com/huggingface/alignment-handbook) immediately, [unsloth](https://github.com/unslothai/unsloth).

## Structure

*View online through viewer.*

## Note

We advise you to reconsider before use, thank you. If you find it useful, please like and follow this account.

## Reference

The **Ghost X** was developed with the goal of researching and developing artificial intelligence useful to humans.

- HuggingFace: [ghost-x](https://huggingface.co/ghost-x)
- Github: [ghost-x-ai](https://github.com/ghost-x-ai)
- X / Twitter: [ghostx_ai](https://twitter.com/ghostx_ai)
- Website: [ghost-x.org](https://ghost-x.org/)

## Citation

```json
@inproceedings{yu-etal-2019-detecting,
title = "Detecting Causal Language Use in Science Findings",
author = "Yu, Bei and
Li, Yingya and
Wang, Jun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1473",
doi = "10.18653/v1/D19-1473",
pages = "4664--4674",
}
```

### ~

原始信息汇总

数据集概述

数据集信息

• 特征:
  • messages:
    • content: 数据类型为 string
    • role: 数据类型为 string
• 分割:
  • train:
    • 字节数: 2396505
    • 样本数: 8676
• 下载大小: 970141 字节
• 数据集大小: 2396505 字节

配置

• 默认配置:
  • 数据文件:
    • train: 路径为 data/train-*

任务类别

• 文本生成
• 文本到文本生成

语言

• 英语

大小类别

• 1K < n < 10K